Supervision 3 (12-13 March, 2020)

Instructions

  1. Read through the instruction carefully. You may face problems if you overlook any of the steps.
  2. The instruction for data collection via APIs is written in Google Colab, a free jupyter environment that requires no setup to use and runs python entirely in the cloud. You need log in with your Google Account to use this free platform. If you do not have Google Account previously, please apply a new account via this link before the supervision. Know more about Google Colab, please check this link.
  3. If you do not have Twitter account, please apply one via this Twitter Signup

Note: functions and filename are highlighted in this document.

Supervision overview

In this exercise, you will familiarise yourself with collect data via Application programming interface(APIs), spatial visualization with collected data and creating a formal map on QGIS.

1. Collect Tweets via API

Apply API from Twitter (5 mins)

The Twitter API (application programming interface) is your gateway to accessing Twitter data. A guidance on setting up Twitter APi: http://social-metrics.org/api-keys/

  1. Open your broswer and open Twitter APIs apply.
  2. Click Apply for a developer account. I strongly recommend you to go through restricted uses of Twitter APIs before applying APIs from twitter.
  3. Log in Twitter with your Twitter account.
  4. Navigate to your account on the menu and click Apps. Then, click create an app button.
  5. In the application page, input information for required items and click create button.
  6. Get your authentication details

Note: please find this page if you fails to apply account during the supervision. notice that these APIs will be expeired after supervision.

API API API API

Collect data using API and save into tweet.csv file (10mins)

In this supervision, we will use python package to help collect data from Twitter via API. If you are new to Python, you may wish to know basic python. Please see the document for python 3.x: https://docs.python.org/3/

The package used in this exercise is Tweepy, a more detailed tutorial: http://docs.tweepy.org/en/latest/.

  • Connect to the twitter RESTful API to access twitter data with Python.
  • Generate custom queries that download tweet data into Python using Tweepy.

Step 0: install tweepy and other essential package

Only install the package at the first time. code for installation:

pip install tweepy

Step 1: import all necessary packages/ libraries

In [1]:
import tweepy
import csv
import time
import os.path

Step 2: define your key

In [2]:
#Variables that contains the user credentials to access Twitter API 
access_token = "528087151-Jh0dy5uVn8O4OAI2E8zh6UtdDv7HemxvnpbrZwmo"
access_token_secret = "94Jbu2xeGEtnXWivKgPgYoKAfzL1zxZAlmHxeWOPpwL8i"
consumer_key = "coXtHK8eQraTJ6vDTZXus1DrW"
consumer_secret = "5mkF2ac563pAJ5Xaw8CuOCtTEmK8OeV0SCgRqjPvZQWtnKseFq"
auth = tweepy.auth.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth)
In [3]:
# test authentication
try:
    api.verify_credentials()
    print("Authentication OK")
except:
    print("Error during authentication")
Authentication OK

Step 3: search Twitter for Tweets and save data to csv

  • what dataset you need? e.g. What is the public sentiment towards coronavirus in London?
  • set keywords e.g. coronavirus, corona virus, virus, covid19, nCoV2019
  • set geocode e.g. Cambridge: 52.2,0.12,2mi, London: 51.53,-0.83,50mi
  • collect tweets from the previous 15 days

https://developer.twitter.com/en/docs/tutorials/filtering-tweets-by-location

The government provide flood information service via this link. Please open the link and check which cities has been issued a flood warining.

flood

In [6]:
file_exists = os.path.isfile('tweet.csv')
keywords = "flood" or "storm*" 
# or "shower*" or "storm*" or "rain"
# keywords = "i"

#Cambridge: 52.2,0.12,2mi
#London: 51.53,-0.83,50mi
search_results = api.search(q=keywords, lang="en", count=80, geocode = "53.96,-1.08,10mi")

print(search_results)

with open('tweet.csv',mode = 'w',encoding='utf-8') as f:
	headers = ['id', 'tweet', 'coordinates']
	writer = csv.DictWriter(f, delimiter=',', lineterminator='\n',fieldnames=headers)
	writer.writeheader()
	for status in search_results:
		print(status)
		if status.coordinates == None:
			writer.writerow({'id': status.id, 'tweet': status.text})
		else:
			writer.writerow({'id': status.id, 'tweet': status.text, 'coordinates': status.coordinates['coordinates']})
[Status(_api=<tweepy.api.API object at 0x10cbcac10>, _json={'created_at': 'Fri Mar 06 20:14:09 +0000 2020', 'id': 1236022277218566144, 'id_str': '1236022277218566144', 'text': 'Mud from the last York flood. Brilliant for gardens and allotments, as I said to a baby pushing York man, who proba… https://t.co/Yv1LE1RmTY', 'truncated': True, 'entities': {'hashtags': [], 'symbols': [], 'user_mentions': [], 'urls': [{'url': 'https://t.co/Yv1LE1RmTY', 'expanded_url': 'https://twitter.com/i/web/status/1236022277218566144', 'display_url': 'twitter.com/i/web/status/1…', 'indices': [117, 140]}]}, 'metadata': {'iso_language_code': 'en', 'result_type': 'recent'}, 'source': '<a href="http://instagram.com" rel="nofollow">Instagram</a>', 'in_reply_to_status_id': None, 'in_reply_to_status_id_str': None, 'in_reply_to_user_id': None, 'in_reply_to_user_id_str': None, 'in_reply_to_screen_name': None, 'user': {'id': 176878577, 'id_str': '176878577', 'name': 'Glyn Watkins', 'screen_name': 'bradwan1', 'location': 'Bradford, West Yorkshire', 'description': 'Poet, writer showman. Fan of pies, pylons, beer & much else. Shows include Pylons Poetry & Pie; Curry & Kipling; and: J.B.Priestley Pie & Proud of Bradford.', 'url': 'http://t.co/0C7AmXkiQc', 'entities': {'url': {'urls': [{'url': 'http://t.co/0C7AmXkiQc', 'expanded_url': 'http://bradwan.co.uk', 'display_url': 'bradwan.co.uk', 'indices': [0, 22]}]}, 'description': {'urls': []}}, 'protected': False, 'followers_count': 130, 'friends_count': 56, 'listed_count': 9, 'created_at': 'Tue Aug 10 17:46:11 +0000 2010', 'favourites_count': 39, 'utc_offset': None, 'time_zone': None, 'geo_enabled': True, 'verified': False, 'statuses_count': 7888, 'lang': None, 'contributors_enabled': False, 'is_translator': False, 'is_translation_enabled': False, 'profile_background_color': 'C0DEED', 'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png', 'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png', 'profile_background_tile': False, 'profile_image_url': 'http://pbs.twimg.com/profile_images/868419890599911426/i9KdF40W_normal.jpg', 'profile_image_url_https': 'https://pbs.twimg.com/profile_images/868419890599911426/i9KdF40W_normal.jpg', 'profile_banner_url': 'https://pbs.twimg.com/profile_banners/176878577/1547727695', 'profile_link_color': '1DA1F2', 'profile_sidebar_border_color': 'C0DEED', 'profile_sidebar_fill_color': 'DDEEF6', 'profile_text_color': '333333', 'profile_use_background_image': True, 'has_extended_profile': False, 'default_profile': True, 'default_profile_image': False, 'following': False, 'follow_request_sent': False, 'notifications': False, 'translator_type': 'none'}, 'geo': {'type': 'Point', 'coordinates': [53.9583, -1.0803]}, 'coordinates': {'type': 'Point', 'coordinates': [-1.0803, 53.9583]}, 'place': {'id': '0c4f58af36a910a6', 'url': 'https://api.twitter.com/1.1/geo/id/0c4f58af36a910a6.json', 'place_type': 'city', 'name': 'York', 'full_name': 'York, England', 'country_code': 'GB', 'country': 'United Kingdom', 'contained_within': [], 'bounding_box': {'type': 'Polygon', 'coordinates': [[[-1.147268, 53.9280019], [-1.0233813, 53.9280019], [-1.0233813, 54.004411], [-1.147268, 54.004411]]]}, 'attributes': {}}, 'contributors': None, 'is_quote_status': False, 'retweet_count': 0, 'favorite_count': 0, 'favorited': False, 'retweeted': False, 'possibly_sensitive': False, 'lang': 'en'}, created_at=datetime.datetime(2020, 3, 6, 20, 14, 9), id=1236022277218566144, id_str='1236022277218566144', text='Mud from the last York flood. Brilliant for gardens and allotments, as I said to a baby pushing York man, who proba… https://t.co/Yv1LE1RmTY', truncated=True, entities={'hashtags': [], 'symbols': [], 'user_mentions': [], 'urls': [{'url': 'https://t.co/Yv1LE1RmTY', 'expanded_url': 'https://twitter.com/i/web/status/1236022277218566144', 'display_url': 'twitter.com/i/web/status/1…', 'indices': [117, 140]}]}, metadata={'iso_language_code': 'en', 'result_type': 'recent'}, source='Instagram', source_url='http://instagram.com', in_reply_to_status_id=None, in_reply_to_status_id_str=None, in_reply_to_user_id=None, in_reply_to_user_id_str=None, in_reply_to_screen_name=None, author=User(_api=<tweepy.api.API object at 0x10cbcac10>, _json={'id': 176878577, 'id_str': '176878577', 'name': 'Glyn Watkins', 'screen_name': 'bradwan1', 'location': 'Bradford, West Yorkshire', 'description': 'Poet, writer showman. Fan of pies, pylons, beer & much else. Shows include Pylons Poetry & Pie; Curry & Kipling; and: J.B.Priestley Pie & Proud of Bradford.', 'url': 'http://t.co/0C7AmXkiQc', 'entities': {'url': {'urls': [{'url': 'http://t.co/0C7AmXkiQc', 'expanded_url': 'http://bradwan.co.uk', 'display_url': 'bradwan.co.uk', 'indices': [0, 22]}]}, 'description': {'urls': []}}, 'protected': False, 'followers_count': 130, 'friends_count': 56, 'listed_count': 9, 'created_at': 'Tue Aug 10 17:46:11 +0000 2010', 'favourites_count': 39, 'utc_offset': None, 'time_zone': None, 'geo_enabled': True, 'verified': False, 'statuses_count': 7888, 'lang': None, 'contributors_enabled': False, 'is_translator': False, 'is_translation_enabled': False, 'profile_background_color': 'C0DEED', 'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png', 'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png', 'profile_background_tile': False, 'profile_image_url': 'http://pbs.twimg.com/profile_images/868419890599911426/i9KdF40W_normal.jpg', 'profile_image_url_https': 'https://pbs.twimg.com/profile_images/868419890599911426/i9KdF40W_normal.jpg', 'profile_banner_url': 'https://pbs.twimg.com/profile_banners/176878577/1547727695', 'profile_link_color': '1DA1F2', 'profile_sidebar_border_color': 'C0DEED', 'profile_sidebar_fill_color': 'DDEEF6', 'profile_text_color': '333333', 'profile_use_background_image': True, 'has_extended_profile': False, 'default_profile': True, 'default_profile_image': False, 'following': False, 'follow_request_sent': False, 'notifications': False, 'translator_type': 'none'}, id=176878577, id_str='176878577', name='Glyn Watkins', screen_name='bradwan1', location='Bradford, West Yorkshire', description='Poet, writer showman. Fan of pies, pylons, beer & much else. Shows include Pylons Poetry & Pie; Curry & Kipling; and: J.B.Priestley Pie & Proud of Bradford.', url='http://t.co/0C7AmXkiQc', entities={'url': {'urls': [{'url': 'http://t.co/0C7AmXkiQc', 'expanded_url': 'http://bradwan.co.uk', 'display_url': 'bradwan.co.uk', 'indices': [0, 22]}]}, 'description': {'urls': []}}, protected=False, followers_count=130, friends_count=56, listed_count=9, created_at=datetime.datetime(2010, 8, 10, 17, 46, 11), favourites_count=39, utc_offset=None, time_zone=None, geo_enabled=True, verified=False, statuses_count=7888, lang=None, contributors_enabled=False, is_translator=False, is_translation_enabled=False, profile_background_color='C0DEED', profile_background_image_url='http://abs.twimg.com/images/themes/theme1/bg.png', profile_background_image_url_https='https://abs.twimg.com/images/themes/theme1/bg.png', profile_background_tile=False, profile_image_url='http://pbs.twimg.com/profile_images/868419890599911426/i9KdF40W_normal.jpg', profile_image_url_https='https://pbs.twimg.com/profile_images/868419890599911426/i9KdF40W_normal.jpg', profile_banner_url='https://pbs.twimg.com/profile_banners/176878577/1547727695', profile_link_color='1DA1F2', profile_sidebar_border_color='C0DEED', profile_sidebar_fill_color='DDEEF6', profile_text_color='333333', profile_use_background_image=True, has_extended_profile=False, default_profile=True, default_profile_image=False, following=False, follow_request_sent=False, notifications=False, translator_type='none'), user=User(_api=<tweepy.api.API object at 0x10cbcac10>, _json={'id': 176878577, 'id_str': '176878577', 'name': 'Glyn Watkins', 'screen_name': 'bradwan1', 'location': 'Bradford, West Yorkshire', 'description': 'Poet, writer showman. Fan of pies, pylons, beer & much else. Shows include Pylons Poetry & Pie; Curry & Kipling; and: J.B.Priestley Pie & Proud of Bradford.', 'url': 'http://t.co/0C7AmXkiQc', 'entities': {'url': {'urls': [{'url': 'http://t.co/0C7AmXkiQc', 'expanded_url': 'http://bradwan.co.uk', 'display_url': 'bradwan.co.uk', 'indices': [0, 22]}]}, 'description': {'urls': []}}, 'protected': False, 'followers_count': 130, 'friends_count': 56, 'listed_count': 9, 'created_at': 'Tue Aug 10 17:46:11 +0000 2010', 'favourites_count': 39, 'utc_offset': None, 'time_zone': None, 'geo_enabled': True, 'verified': False, 'statuses_count': 7888, 'lang': None, 'contributors_enabled': False, 'is_translator': False, 'is_translation_enabled': False, 'profile_background_color': 'C0DEED', 'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png', 'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png', 'profile_background_tile': False, 'profile_image_url': 'http://pbs.twimg.com/profile_images/868419890599911426/i9KdF40W_normal.jpg', 'profile_image_url_https': 'https://pbs.twimg.com/profile_images/868419890599911426/i9KdF40W_normal.jpg', 'profile_banner_url': 'https://pbs.twimg.com/profile_banners/176878577/1547727695', 'profile_link_color': '1DA1F2', 'profile_sidebar_border_color': 'C0DEED', 'profile_sidebar_fill_color': 'DDEEF6', 'profile_text_color': '333333', 'profile_use_background_image': True, 'has_extended_profile': False, 'default_profile': True, 'default_profile_image': False, 'following': False, 'follow_request_sent': False, 'notifications': False, 'translator_type': 'none'}, id=176878577, id_str='176878577', name='Glyn Watkins', screen_name='bradwan1', location='Bradford, West Yorkshire', description='Poet, writer showman. 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coordinates=[[[-1.147268, 53.9280019], [-1.0233813, 53.9280019], [-1.0233813, 54.004411], [-1.147268, 54.004411]]]), attributes={}), contributors=None, is_quote_status=False, retweet_count=0, favorite_count=0, favorited=False, retweeted=False, possibly_sensitive=False, lang='en'), Status(_api=<tweepy.api.API object at 0x10cbcac10>, _json={'created_at': 'Fri Mar 06 16:58:09 +0000 2020', 'id': 1235972950555230208, 'id_str': '1235972950555230208', 'text': 'Flood Community Drop in session Monday 5-7pm 16th March @ Friargate Quaker Meeting House. https://t.co/nAZNmto7Au', 'truncated': False, 'entities': {'hashtags': [], 'symbols': [], 'user_mentions': [], 'urls': [], 'media': [{'id': 1235972728882110464, 'id_str': '1235972728882110464', 'indices': [90, 113], 'media_url': 'http://pbs.twimg.com/media/EScOpgEWoAAmgQz.jpg', 'media_url_https': 'https://pbs.twimg.com/media/EScOpgEWoAAmgQz.jpg', 'url': 'https://t.co/nAZNmto7Au', 'display_url': 'pic.twitter.com/nAZNmto7Au', 'expanded_url': 'https://twitter.com/GuildhallWard/status/1235972950555230208/photo/1', 'type': 'photo', 'sizes': {'thumb': {'w': 150, 'h': 150, 'resize': 'crop'}, 'large': {'w': 2000, 'h': 1408, 'resize': 'fit'}, 'small': {'w': 680, 'h': 479, 'resize': 'fit'}, 'medium': {'w': 1200, 'h': 845, 'resize': 'fit'}}}]}, 'extended_entities': {'media': [{'id': 1235972728882110464, 'id_str': '1235972728882110464', 'indices': [90, 113], 'media_url': 'http://pbs.twimg.com/media/EScOpgEWoAAmgQz.jpg', 'media_url_https': 'https://pbs.twimg.com/media/EScOpgEWoAAmgQz.jpg', 'url': 'https://t.co/nAZNmto7Au', 'display_url': 'pic.twitter.com/nAZNmto7Au', 'expanded_url': 'https://twitter.com/GuildhallWard/status/1235972950555230208/photo/1', 'type': 'photo', 'sizes': {'thumb': {'w': 150, 'h': 150, 'resize': 'crop'}, 'large': {'w': 2000, 'h': 1408, 'resize': 'fit'}, 'small': {'w': 680, 'h': 479, 'resize': 'fit'}, 'medium': {'w': 1200, 'h': 845, 'resize': 'fit'}}}, {'id': 1235972764751839240, 'id_str': 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'location': 'York', 'description': 'Guildhall Ward covers inside the Bar Walls (north of the Ouse), Layerthorpe, Foss Islands, St Nicholas Fields, Bootham, The Groves and Haxby Road to White Cross', 'url': 'https://t.co/QZASoWSzo8', 'entities': {'url': {'urls': [{'url': 'https://t.co/QZASoWSzo8', 'expanded_url': 'http://york.gov.uk/guildhallward', 'display_url': 'york.gov.uk/guildhallward', 'indices': [0, 23]}]}, 'description': {'urls': []}}, 'protected': False, 'followers_count': 467, 'friends_count': 357, 'listed_count': 8, 'created_at': 'Tue Feb 04 16:30:45 +0000 2014', 'favourites_count': 1, 'utc_offset': None, 'time_zone': None, 'geo_enabled': False, 'verified': False, 'statuses_count': 258, 'lang': None, 'contributors_enabled': False, 'is_translator': False, 'is_translation_enabled': False, 'profile_background_color': 'C0DEED', 'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png', 'profile_background_image_url_https': 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notifications=False, translator_type='none'), geo={'type': 'Point', 'coordinates': [53.996804, -1.169873]}, coordinates={'type': 'Point', 'coordinates': [-1.169873, 53.996804]}, place=Place(_api=<tweepy.api.API object at 0x10cbcac10>, id='4efd6fc8c50fae33', url='https://api.twitter.com/1.1/geo/id/4efd6fc8c50fae33.json', place_type='admin', name='Yorkshire and The Humber', full_name='Yorkshire and The Humber, England', country_code='GB', country='United Kingdom', contained_within=[], bounding_box=BoundingBox(_api=<tweepy.api.API object at 0x10cbcac10>, type='Polygon', coordinates=[[[-2.56475248726412, 53.3015341502953], [0.149787002205902, 53.3015341502953], [0.149787002205902, 54.5621550270294], [-2.56475248726412, 54.5621550270294]]]), attributes={}), contributors=None, is_quote_status=False, retweet_count=0, favorite_count=0, favorited=False, retweeted=False, possibly_sensitive=False, lang='en')

2. Sentiment analysis with content of tweets

  • the process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer's attitude towards a particular topic, product, etc. is positive, negative, or neutral
  • an example of AFINN dictionary https://github.com/fnielsen/afinn
  • analyse the sentiment of tweets to show Londoners' attitude towards coronavirus

Step 0 & 1: install and import necessary packages

In [7]:
from __future__ import division
from afinn import Afinn
import numpy as np
import pandas as pd
# importing the necessary dependencies
from collections import Counter
from matplotlib import pyplot as plt
from bs4 import BeautifulSoup
from nltk.corpus import wordnet, stopwords
from spacy.lang.en.stop_words import STOP_WORDS
from nltk.tokenize import WordPunctTokenizer
import re
#import seaborn as sns
#import pandas as pd
import nltk
import warnings
#nltk.download('all')
warnings.filterwarnings("ignore")
%matplotlib inline

An example of sentiment analysis - AFINN

In [8]:
afinn = Afinn(emoticons = True)
afinn.score('This is utterly excellent!;)')
Out[8]:
5.0

Step 2: remove duplicate tweets

In [9]:
df = pd.read_csv('tweet.csv')
df.drop_duplicates(subset ="tweet", keep = False, inplace = True)
df
Out[9]:
id tweet coordinates
0 1236022277218566144 Mud from the last York flood. Brilliant for ga... [-1.0803, 53.9583]
1 1235972950555230208 Flood Community Drop in session Monday 5-7pm 1... NaN
2 1235962305545977856 RT @FishergateYork: Flood Community Drop in se... NaN
3 1235946017067057152 First up was Stamford Bridge. Some #flooddefen... NaN
4 1235935231007485954 Flood Community Drop in session Wedneday 11th ... NaN
5 1235926940495867905 @HouseofCommons @JulianSturdy Hi @JulianSturdy... NaN
6 1235924364111081472 With an unsettled outlook for the weekend our ... NaN
7 1235921488101937153 RT @vikloumcc: Come on #York. I’ve met so many... NaN
8 1235908081109827590 Flood Alert: Tidal River Ouse from Naburn Lock... [-1.11412, 53.822911]
9 1235885403917385728 @HighFarndale Not good in terms of environment... NaN
10 1235879276622331904 @4TheArsenal_ Personally flood he hid from thi... NaN
11 1235861179450290177 NEWS - Grants of up to £5,000 are being offere... NaN
12 1235845935650836482 8:30am - Temple Hirst Junction \nHaven't been ... NaN
13 1235839866555916288 RT @Mindlockyork: Check out the boss lady chat... NaN
18 1235684109269131264 Flood Alert: Tidal River Ouse from Naburn Lock... [-1.11412, 53.822911]
20 1235681865262075906 There is still plenty of cleaning up to do, st... NaN
21 1235681852867870721 @NorthYorksFire responded to lots of Flood inc... NaN
22 1235681828197085185 On 9th Feb the @EnvAgency issued a Flood Alert... NaN
24 1235674944861802496 Come on #York. I’ve met so many who have exper... NaN
27 1235649502498840576 Bingo Update.. (see below) 🥺\n\nNow it’s time ... NaN
30 1235642535608864768 RT @YorkGreens: Cllr @DeniseCraghill explains ... NaN
32 1235637442838966273 @floodmary @InnesThomson @pinktrackies @ADA_up... NaN
33 1235635793793867777 @floodmary @InnesThomson @pinktrackies @ADA_up... NaN
34 1235635720578007041 @floodmary @InnesThomson @pinktrackies @ADA_up... NaN
35 1235598769543991302 Flood Alert: Tidal River Ouse from Naburn Lock... [-1.11412, 53.822911]
36 1235596055607160834 Back to normal: River Ouse at Naburn Lock 15:4... [-1.096129, 53.893899]
37 1235596032861507586 Back to normal: Upper River Ouse 15:45 5 March... [-1.169873, 53.996804]
38 1235586941569163264 @JulianSturdy @ThomlinsonAlfie @yorkpress @Cit... NaN
39 1235584978978775044 Julie Foley on the Flood &amp; Coastal Erosion... NaN
40 1235583883220934659 RT @GSwinburn: Hi @NigelCllr can you confirm i... NaN
41 1235578669684252678 RT @YorkshirePFR: @CityofYork Council Flood Ri... NaN
42 1235578487798300677 Thanks @FloodframeUk for sharing information a... NaN
43 1235577853632266242 @CityofYork Council Flood Risk Manager and @Yo... NaN
44 1235567773956349952 Boris Johnson defends decision not to visit UK... NaN
45 1235567346342715394 Feelings flood your interactions with others t... NaN
46 1235565759637721095 RT @vikloumcc: Today I’m at the @NatFloodForum... NaN
48 1235533364846833666 Route 7 flood update - East / West Cowick is n... NaN
49 1235526957179133953 NEWS - 10,000 litres of water is being pumped ... NaN
51 1235518102105919495 Hi @NigelCllr can you confirm if @cityofyork w... NaN
52 1235512928704647168 Some key messages from the Minister of Floodin... NaN
53 1235505901613826048 Today I’m at the @NatFloodForum conference to ... NaN
57 1235495211494764547 It's great to see New Walk being hosed down so... NaN
58 1235493008512073728 @DWildheart Okay: '...Flood' I was already fam... NaN
59 1235482864030027778 Flood Warning: River Ouse at Naburn Lock 08:01... [-1.096129, 53.893899]
60 1235482841267556353 Flood Alert: Upper River Ouse 08:11 5 March ht... [-1.169873, 53.996804]
61 1235482818555215873 Flood Alert: Tidal River Ouse from Naburn Lock... [-1.11412, 53.822911]
62 1235358937937063939 RT @RobStoneman: Protect cities from flooding ... NaN
66 1235316708715397120 Flood Warning: River Ouse at Naburn Lock 21:01... [-1.096129, 53.893899]
67 1235316685831315464 Flood Alert: Upper River Ouse 21:04 4 March ht... [-1.169873, 53.996804]
68 1235311644194074626 Flood Alert: Tidal River Ouse from Naburn Lock... [-1.11412, 53.822911]
73 1235291158923956227 @PatersonLou The flood barriers aren't working... NaN
75 1235252399121539073 RT @RiverOuseYork: River Ouse York water level... NaN
77 1235248670355972102 River Ouse York water level: 2.12m at 16:00. C... NaN
78 1235237319558475777 RT @RiverOuseYork: River Ouse York water level... NaN
79 1235233686649528320 Flood Alert: Upper River Ouse 15:35 4 March ht... [-1.169873, 53.996804]

Step 3: clean data

  • remove unuseful characters and hyper links
In [10]:
#removing all the unuseful characters such as "@,#" and hyper links "https...."
tok = WordPunctTokenizer()
pat1 = r'@[A-Za-z0-9]+'       # Removing @text situations
pat2 = r'https?://[A-Za-z0-9./]+'  # Removing hyperlinks
combined_pat = r'|'.join((pat1, pat2))  

def tweet_cleaner(text):
    soup = BeautifulSoup(text, 'lxml') # converting possibly unconverted html to text
    souped = soup.get_text()
    stripped = re.sub(combined_pat, '', souped)
    letters_only = re.sub("[^a-zA-Z]", " ", stripped) # Getting read of non text characters
    lower_case = letters_only.lower()
    # During the letters_only process two lines above, it has created unnecessay white spaces,
    # We tokenize and join together to remove unneccessary white spaces
    words = tok.tokenize(lower_case)
    return (" ".join(words)).strip()

# Looping over every tweet in the data set and applying the tweet_cleaner function it.
clean_tweet_texts = []
for tweet in df.tweet:
    clean_tweet_texts.append(tweet_cleaner(tweet))
clean_tweet_texts

#creating a new DataFrame for the filtered text
df['clean_tweet'] = clean_tweet_texts
df
Out[10]:
id tweet coordinates clean_tweet
0 1236022277218566144 Mud from the last York flood. Brilliant for ga... [-1.0803, 53.9583] mud from the last york flood brilliant for gar...
1 1235972950555230208 Flood Community Drop in session Monday 5-7pm 1... NaN flood community drop in session monday pm th m...
2 1235962305545977856 RT @FishergateYork: Flood Community Drop in se... NaN rt flood community drop in session wedneday th...
3 1235946017067057152 First up was Stamford Bridge. Some #flooddefen... NaN first up was stamford bridge some flooddefence...
4 1235935231007485954 Flood Community Drop in session Wedneday 11th ... NaN flood community drop in session wedneday th ma...
5 1235926940495867905 @HouseofCommons @JulianSturdy Hi @JulianSturdy... NaN hi looking forward to hearing why you voted ag...
6 1235924364111081472 With an unsettled outlook for the weekend our ... NaN with an unsettled outlook for the weekend our ...
7 1235921488101937153 RT @vikloumcc: Come on #York. I’ve met so many... NaN rt come on york i ve met so many who have expe...
8 1235908081109827590 Flood Alert: Tidal River Ouse from Naburn Lock... [-1.11412, 53.822911] flood alert tidal river ouse from naburn lock ...
9 1235885403917385728 @HighFarndale Not good in terms of environment... NaN not good in terms of environment wildlife clim...
10 1235879276622331904 @4TheArsenal_ Personally flood he hid from thi... NaN personally flood he hid from this he s bumblin...
11 1235861179450290177 NEWS - Grants of up to £5,000 are being offere... NaN news grants of up to are being offered to floo...
12 1235845935650836482 8:30am - Temple Hirst Junction \nHaven't been ... NaN am temple hirst junction haven t been able to ...
13 1235839866555916288 RT @Mindlockyork: Check out the boss lady chat... NaN rt check out the boss lady chatting to from mi...
18 1235684109269131264 Flood Alert: Tidal River Ouse from Naburn Lock... [-1.11412, 53.822911] flood alert tidal river ouse from naburn lock ...
20 1235681865262075906 There is still plenty of cleaning up to do, st... NaN there is still plenty of cleaning up to do sta...
21 1235681852867870721 @NorthYorksFire responded to lots of Flood inc... NaN responded to lots of flood incidents including...
22 1235681828197085185 On 9th Feb the @EnvAgency issued a Flood Alert... NaN on th feb the issued a flood alert for the upp...
24 1235674944861802496 Come on #York. I’ve met so many who have exper... NaN come on york i ve met so many who have experie...
27 1235649502498840576 Bingo Update.. (see below) 🥺\n\nNow it’s time ... NaN bingo update see below now it s time to raise ...
30 1235642535608864768 RT @YorkGreens: Cllr @DeniseCraghill explains ... NaN rt cllr explains what are doing in york to tac...
32 1235637442838966273 @floodmary @InnesThomson @pinktrackies @ADA_up... NaN updates
33 1235635793793867777 @floodmary @InnesThomson @pinktrackies @ADA_up... NaN updates
34 1235635720578007041 @floodmary @InnesThomson @pinktrackies @ADA_up... NaN updates
35 1235598769543991302 Flood Alert: Tidal River Ouse from Naburn Lock... [-1.11412, 53.822911] flood alert tidal river ouse from naburn lock ...
36 1235596055607160834 Back to normal: River Ouse at Naburn Lock 15:4... [-1.096129, 53.893899] back to normal river ouse at naburn lock march
37 1235596032861507586 Back to normal: Upper River Ouse 15:45 5 March... [-1.169873, 53.996804] back to normal upper river ouse march
38 1235586941569163264 @JulianSturdy @ThomlinsonAlfie @yorkpress @Cit... NaN from the majority of mps vo
39 1235584978978775044 Julie Foley on the Flood &amp; Coastal Erosion... NaN julie foley on the flood coastal erosion risk ...
40 1235583883220934659 RT @GSwinburn: Hi @NigelCllr can you confirm i... NaN rt hi can you confirm if was able to access th...
41 1235578669684252678 RT @YorkshirePFR: @CityofYork Council Flood Ri... NaN rt council flood risk manager and pm steve wra...
42 1235578487798300677 Thanks @FloodframeUk for sharing information a... NaN thanks for sharing information about our surve...
43 1235577853632266242 @CityofYork Council Flood Risk Manager and @Yo... NaN council flood risk manager and pm steve wragg ...
44 1235567773956349952 Boris Johnson defends decision not to visit UK... NaN boris johnson defends decision not to visit uk...
45 1235567346342715394 Feelings flood your interactions with others t... NaN feelings flood your interactions with others t...
46 1235565759637721095 RT @vikloumcc: Today I’m at the @NatFloodForum... NaN rt today i m at the conference to learn and sh...
48 1235533364846833666 Route 7 flood update - East / West Cowick is n... NaN route flood update east west cowick is now ope...
49 1235526957179133953 NEWS - 10,000 litres of water is being pumped ... NaN news litres of water is being pumped out of lo...
51 1235518102105919495 Hi @NigelCllr can you confirm if @cityofyork w... NaN hi can you confirm if was able to access the b...
52 1235512928704647168 Some key messages from the Minister of Floodin... NaN some key messages from the minister of floodin...
53 1235505901613826048 Today I’m at the @NatFloodForum conference to ... NaN today i m at the conference to learn and share...
57 1235495211494764547 It's great to see New Walk being hosed down so... NaN it s great to see new walk being hosed down so...
58 1235493008512073728 @DWildheart Okay: '...Flood' I was already fam... NaN okay flood i was already familiar with and i r...
59 1235482864030027778 Flood Warning: River Ouse at Naburn Lock 08:01... [-1.096129, 53.893899] flood warning river ouse at naburn lock march
60 1235482841267556353 Flood Alert: Upper River Ouse 08:11 5 March ht... [-1.169873, 53.996804] flood alert upper river ouse march
61 1235482818555215873 Flood Alert: Tidal River Ouse from Naburn Lock... [-1.11412, 53.822911] flood alert tidal river ouse from naburn lock ...
62 1235358937937063939 RT @RobStoneman: Protect cities from flooding ... NaN rt protect cities from flooding by making them...
66 1235316708715397120 Flood Warning: River Ouse at Naburn Lock 21:01... [-1.096129, 53.893899] flood warning river ouse at naburn lock march
67 1235316685831315464 Flood Alert: Upper River Ouse 21:04 4 March ht... [-1.169873, 53.996804] flood alert upper river ouse march
68 1235311644194074626 Flood Alert: Tidal River Ouse from Naburn Lock... [-1.11412, 53.822911] flood alert tidal river ouse from naburn lock ...
73 1235291158923956227 @PatersonLou The flood barriers aren't working... NaN the flood barriers aren t working so the govt ...
75 1235252399121539073 RT @RiverOuseYork: River Ouse York water level... NaN rt river ouse york water level m at chance of ...
77 1235248670355972102 River Ouse York water level: 2.12m at 16:00. C... NaN river ouse york water level m at chance of flo...
78 1235237319558475777 RT @RiverOuseYork: River Ouse York water level... NaN rt river ouse york water level m at chance of ...
79 1235233686649528320 Flood Alert: Upper River Ouse 15:35 4 March ht... [-1.169873, 53.996804] flood alert upper river ouse march

step 3: score tweets and save to a new csv

In [12]:
# Sentiment analysis with AFINN
afinn = Afinn(emoticons=True)
afinn_scores = [afinn.score(clean_tweet) for clean_tweet in df.clean_tweet]
df['sentiment'] = afinn_scores
#save to a new csv
#df.to_csv('tweet_scores.csv')
df
Out[12]:
id tweet coordinates clean_tweet sentiment
0 1236022277218566144 Mud from the last York flood. Brilliant for ga... [-1.0803, 53.9583] mud from the last york flood brilliant for gar... 4.0
1 1235972950555230208 Flood Community Drop in session Monday 5-7pm 1... NaN flood community drop in session monday pm th m... -1.0
2 1235962305545977856 RT @FishergateYork: Flood Community Drop in se... NaN rt flood community drop in session wedneday th... -1.0
3 1235946017067057152 First up was Stamford Bridge. Some #flooddefen... NaN first up was stamford bridge some flooddefence... 1.0
4 1235935231007485954 Flood Community Drop in session Wedneday 11th ... NaN flood community drop in session wedneday th ma... -1.0
5 1235926940495867905 @HouseofCommons @JulianSturdy Hi @JulianSturdy... NaN hi looking forward to hearing why you voted ag... 2.0
6 1235924364111081472 With an unsettled outlook for the weekend our ... NaN with an unsettled outlook for the weekend our ... -1.0
7 1235921488101937153 RT @vikloumcc: Come on #York. I’ve met so many... NaN rt come on york i ve met so many who have expe... 6.0
8 1235908081109827590 Flood Alert: Tidal River Ouse from Naburn Lock... [-1.11412, 53.822911] flood alert tidal river ouse from naburn lock ... -1.0
9 1235885403917385728 @HighFarndale Not good in terms of environment... NaN not good in terms of environment wildlife clim... -2.0
10 1235879276622331904 @4TheArsenal_ Personally flood he hid from thi... NaN personally flood he hid from this he s bumblin... -1.0
11 1235861179450290177 NEWS - Grants of up to £5,000 are being offere... NaN news grants of up to are being offered to floo... -2.0
12 1235845935650836482 8:30am - Temple Hirst Junction \nHaven't been ... NaN am temple hirst junction haven t been able to ... -3.0
13 1235839866555916288 RT @Mindlockyork: Check out the boss lady chat... NaN rt check out the boss lady chatting to from mi... 0.0
18 1235684109269131264 Flood Alert: Tidal River Ouse from Naburn Lock... [-1.11412, 53.822911] flood alert tidal river ouse from naburn lock ... -1.0
20 1235681865262075906 There is still plenty of cleaning up to do, st... NaN there is still plenty of cleaning up to do sta... 0.0
21 1235681852867870721 @NorthYorksFire responded to lots of Flood inc... NaN responded to lots of flood incidents including... 0.0
22 1235681828197085185 On 9th Feb the @EnvAgency issued a Flood Alert... NaN on th feb the issued a flood alert for the upp... -1.0
24 1235674944861802496 Come on #York. I’ve met so many who have exper... NaN come on york i ve met so many who have experie... 6.0
27 1235649502498840576 Bingo Update.. (see below) 🥺\n\nNow it’s time ... NaN bingo update see below now it s time to raise ... 1.0
30 1235642535608864768 RT @YorkGreens: Cllr @DeniseCraghill explains ... NaN rt cllr explains what are doing in york to tac... 0.0
32 1235637442838966273 @floodmary @InnesThomson @pinktrackies @ADA_up... NaN updates 0.0
33 1235635793793867777 @floodmary @InnesThomson @pinktrackies @ADA_up... NaN updates 0.0
34 1235635720578007041 @floodmary @InnesThomson @pinktrackies @ADA_up... NaN updates 0.0
35 1235598769543991302 Flood Alert: Tidal River Ouse from Naburn Lock... [-1.11412, 53.822911] flood alert tidal river ouse from naburn lock ... -1.0
36 1235596055607160834 Back to normal: River Ouse at Naburn Lock 15:4... [-1.096129, 53.893899] back to normal river ouse at naburn lock march 0.0
37 1235596032861507586 Back to normal: Upper River Ouse 15:45 5 March... [-1.169873, 53.996804] back to normal upper river ouse march 0.0
38 1235586941569163264 @JulianSturdy @ThomlinsonAlfie @yorkpress @Cit... NaN from the majority of mps vo 0.0
39 1235584978978775044 Julie Foley on the Flood &amp; Coastal Erosion... NaN julie foley on the flood coastal erosion risk ... -2.0
40 1235583883220934659 RT @GSwinburn: Hi @NigelCllr can you confirm i... NaN rt hi can you confirm if was able to access th... -1.0
41 1235578669684252678 RT @YorkshirePFR: @CityofYork Council Flood Ri... NaN rt council flood risk manager and pm steve wra... -2.0
42 1235578487798300677 Thanks @FloodframeUk for sharing information a... NaN thanks for sharing information about our surve... 2.0
43 1235577853632266242 @CityofYork Council Flood Risk Manager and @Yo... NaN council flood risk manager and pm steve wragg ... -2.0
44 1235567773956349952 Boris Johnson defends decision not to visit UK... NaN boris johnson defends decision not to visit uk... -4.0
45 1235567346342715394 Feelings flood your interactions with others t... NaN feelings flood your interactions with others t... 3.0
46 1235565759637721095 RT @vikloumcc: Today I’m at the @NatFloodForum... NaN rt today i m at the conference to learn and sh... -1.0
48 1235533364846833666 Route 7 flood update - East / West Cowick is n... NaN route flood update east west cowick is now ope... 0.0
49 1235526957179133953 NEWS - 10,000 litres of water is being pumped ... NaN news litres of water is being pumped out of lo... 0.0
51 1235518102105919495 Hi @NigelCllr can you confirm if @cityofyork w... NaN hi can you confirm if was able to access the b... -1.0
52 1235512928704647168 Some key messages from the Minister of Floodin... NaN some key messages from the minister of floodin... 0.0
53 1235505901613826048 Today I’m at the @NatFloodForum conference to ... NaN today i m at the conference to learn and share... -1.0
57 1235495211494764547 It's great to see New Walk being hosed down so... NaN it s great to see new walk being hosed down so... 3.0
58 1235493008512073728 @DWildheart Okay: '...Flood' I was already fam... NaN okay flood i was already familiar with and i r... 1.0
59 1235482864030027778 Flood Warning: River Ouse at Naburn Lock 08:01... [-1.096129, 53.893899] flood warning river ouse at naburn lock march -3.0
60 1235482841267556353 Flood Alert: Upper River Ouse 08:11 5 March ht... [-1.169873, 53.996804] flood alert upper river ouse march -1.0
61 1235482818555215873 Flood Alert: Tidal River Ouse from Naburn Lock... [-1.11412, 53.822911] flood alert tidal river ouse from naburn lock ... -1.0
62 1235358937937063939 RT @RobStoneman: Protect cities from flooding ... NaN rt protect cities from flooding by making them... 5.0
66 1235316708715397120 Flood Warning: River Ouse at Naburn Lock 21:01... [-1.096129, 53.893899] flood warning river ouse at naburn lock march -3.0
67 1235316685831315464 Flood Alert: Upper River Ouse 21:04 4 March ht... [-1.169873, 53.996804] flood alert upper river ouse march -1.0
68 1235311644194074626 Flood Alert: Tidal River Ouse from Naburn Lock... [-1.11412, 53.822911] flood alert tidal river ouse from naburn lock ... -1.0
73 1235291158923956227 @PatersonLou The flood barriers aren't working... NaN the flood barriers aren t working so the govt ... -1.0
75 1235252399121539073 RT @RiverOuseYork: River Ouse York water level... NaN rt river ouse york water level m at chance of ... 1.0
77 1235248670355972102 River Ouse York water level: 2.12m at 16:00. C... NaN river ouse york water level m at chance of flo... 1.0
78 1235237319558475777 RT @RiverOuseYork: River Ouse York water level... NaN rt river ouse york water level m at chance of ... 1.0
79 1235233686649528320 Flood Alert: Upper River Ouse 15:35 4 March ht... [-1.169873, 53.996804] flood alert upper river ouse march -1.0

Step 4: basic statistic analysis of sentiment result

In [13]:
df['sentiment'].describe()
Out[13]:
count    55.000000
mean     -0.090909
std       2.048240
min      -4.000000
25%      -1.000000
50%      -1.000000
75%       0.500000
max       6.000000
Name: sentiment, dtype: float64

3. Discourse analysis

  • topics of interest
  • wordclouds

English keyword extractions

For the detail of further cleaning tweets, please see https://github.com/sillsphelyx/Twitter_text_cleaning

Step 1: filter English tweets (remove)

In [15]:
#creating a new DataFrame for the filtered text
clean_df = pd.DataFrame(df['id'],columns=['id'])
clean_df['text'] = clean_tweet_texts

# Removing non-english text
#  This function takes in as input a sentence and returns only english words 
def filter_for_english(sent):
    return (" ".join(w for w in nltk.wordpunct_tokenize(sent) \
            if len(wordnet.synsets(w)) > 3 ))
#looping over all tweets to return contained english words
checked_for_english = [filter_for_english(sent) for sent in clean_df.text]
# adding a column with tweets that are in English
clean_df['English_text'] = checked_for_english
# adding count columns for number of words per tweet for both clean_text and English only text 
clean_df['Clean_text_len'] = [len(t.split()) for t in clean_df.text]
clean_df['English_word_count'] = [len(t.split()) for t in clean_df.English_text] # Useful for filtering dataframe on english text
#Filter dataframe by choosing rows that have atleast one english word
english_df = clean_df[clean_df['English_word_count'] > 0][['id', 'English_text']]
english_df.head()
clean_df
Out[15]:
id text English_text Clean_text_len English_word_count
0 1236022277218566144 mud from the last york flood brilliant for gar... mud last flood brilliant gardens as i said a b... 22 12
1 1235972950555230208 flood community drop in session monday pm th m... flood community drop in session pm march meeti... 13 9
2 1235962305545977856 rt flood community drop in session wedneday th... flood community drop in session march pm church 16 8
3 1235946017067057152 first up was stamford bridge some flooddefence... first up was bridge some still in place can cl... 20 13
4 1235935231007485954 flood community drop in session wedneday th ma... flood community drop in session march pm church 15 8
5 1235926940495867905 hi looking forward to hearing why you voted ag... looking forward hearing voted a motion 13 6
6 1235924364111081472 with an unsettled outlook for the weekend our ... unsettled area incident rooms in will be 18 7
7 1235921488101937153 rt come on york i ve met so many who have expe... come on i met so have experienced flooding is ... 24 13
8 1235908081109827590 flood alert tidal river ouse from naburn lock ... flood alert lock march 11 4
9 1235885403917385728 not good in terms of environment wildlife clim... good in terms change flood 12 5
10 1235879276622331904 personally flood he hid from this he s bumblin... personally flood hid s bumbling way through 12 7
11 1235861179450290177 news grants of up to are being offered to floo... news grants up are being offered flood in 17 8
12 1235845935650836482 am temple hirst junction haven t been able to ... am temple junction t been able see flood plain... 23 12
13 1235839866555916288 rt check out the boss lady chatting to from mi... check out boss about flood scares a number b 21 9
18 1235684109269131264 flood alert tidal river ouse from naburn lock ... flood alert lock march 11 4
20 1235681865262075906 there is still plenty of cleaning up to do sta... there is still cleaning up do standing water o... 23 14
21 1235681852867870721 responded to lots of flood incidents including... lots flood including flooded properties found 14 6
22 1235681828197085185 on th feb the issued a flood alert for the upp... on issued a flood alert upper as a result due ... 22 11
24 1235674944861802496 come on york i ve met so many who have experie... come on i met so have experienced flooding is ... 22 13
27 1235649502498840576 bingo update see below now it s time to raise ... update see below now s time raise as much as p... 20 14
30 1235642535608864768 rt cllr explains what are doing in york to tac... are doing in tackle flooding need invest more ... 18 10
32 1235637442838966273 updates updates 1 1
33 1235635793793867777 updates updates 1 1
34 1235635720578007041 updates updates 1 1
35 1235598769543991302 flood alert tidal river ouse from naburn lock ... flood alert lock march 11 4
36 1235596055607160834 back to normal river ouse at naburn lock march back normal lock march 9 4
37 1235596032861507586 back to normal upper river ouse march back normal upper march 7 4
38 1235586941569163264 from the majority of mps vo 6 0
39 1235584978978775044 julie foley on the flood coastal erosion risk ... on flood erosion risk creating places g 16 7
40 1235583883220934659 rt hi can you confirm if was able to access th... can confirm was able access scheme pay extende... 21 10
41 1235578669684252678 rt council flood risk manager and pm steve wra... flood risk pm future flood protection discuss 16 7
42 1235578487798300677 thanks for sharing information about our surve... sharing information about survey 12 4
43 1235577853632266242 council flood risk manager and pm steve wragg ... flood risk pm future flood protection d 15 7
44 1235567773956349952 boris johnson defends decision not to visit uk... defends decision visit s flood hit areas servi... 21 11
45 1235567346342715394 feelings flood your interactions with others t... feelings flood today a great more sagittarius 13 7
46 1235565759637721095 rt today i m at the conference to learn and sh... today i m learn share experience working toget... 20 11
48 1235533364846833666 route flood update east west cowick is now ope... route flood update east west is now open bus w... 20 13
49 1235526957179133953 news litres of water is being pumped out of lo... news water is being pumped out lower second sa... 17 10
51 1235518102105919495 hi can you confirm if was able to access the b... can confirm was able access scheme pay extende... 20 10
52 1235512928704647168 some key messages from the minister of floodin... some key messages minister flooding capital in... 21 8
53 1235505901613826048 today i m at the conference to learn and share... today i m learn share experience working toget... 18 11
57 1235495211494764547 it s great to see new walk being hosed down so... s great see new walk being down so flood water... 22 13
58 1235493008512073728 okay flood i was already familiar with and i r... okay flood i was familiar i realized weather o... 18 10
59 1235482864030027778 flood warning river ouse at naburn lock march flood warning lock march 8 4
60 1235482841267556353 flood alert upper river ouse march flood alert upper march 6 4
61 1235482818555215873 flood alert tidal river ouse from naburn lock ... flood alert lock march 11 4
62 1235358937937063939 rt protect cities from flooding by making them... flooding making wilder better people make spac... 22 9
66 1235316708715397120 flood warning river ouse at naburn lock march flood warning lock march 8 4
67 1235316685831315464 flood alert upper river ouse march flood alert upper march 6 4
68 1235311644194074626 flood alert tidal river ouse from naburn lock ... flood alert lock march 11 4
73 1235291158923956227 the flood barriers aren t working so the govt ... flood t working so have told try blocking roll 19 9
75 1235252399121539073 rt river ouse york water level m at chance of ... water level m chance flooding flood falling 15 7
77 1235248670355972102 river ouse york water level m at chance of flo... water level m chance flooding flood falling 14 7
78 1235237319558475777 rt river ouse york water level m at chance of ... water level m chance flooding flood falling 15 7
79 1235233686649528320 flood alert upper river ouse march flood alert upper march 6 4

Step 2: Wordcloud generation

In [23]:
#generate new wordcloud
from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
text = clean_df.English_text
wordcloud = WordCloud(
    width = 3000,
    height = 2000,
    background_color = 'black',
    stopwords = STOPWORDS).generate(str(text))
fig = plt.figure(
    figsize = (40, 30),
    facecolor = 'k',
    edgecolor = 'k')
plt.imshow(wordcloud, interpolation = 'bilinear')
plt.axis('off')
plt.tight_layout(pad=0)
plt.show()
#plt.savefig("wordcloud.png")

Step 3: most used words in the twitter data¶

In [18]:
## Inspecting the most common words used in all the observed english tweets
# Breaking into individual words every tweet
def my_tokenizer(text):
    return text.split() if text != None else []
# Breaking all english tweets into individual words and returning a list of these words
tokens = english_df.English_text.map(my_tokenizer).sum()
# most common words in all tweets (stop words included)
counter_wsw = Counter(tokens)
counter_wsw.most_common(20)
# creating a data frame for the most common words with stop words in the tweets for plotting
# >2 to exclude single and doulble lettered words that aren't included among stop words 
word_freq_wsw = pd.DataFrame.from_records([x for x in counter_wsw.most_common(30) if len(x[0])> 2], columns = ['tokens', 'count'])
# removal of stop words and rechecking the most frequent words
def remove_stopwords(tokens):
    return [t for t in tokens if t not in STOP_WORDS]
counter = Counter(remove_stopwords(tokens))
# creating a data frame for the most common words without stop words in the tweets for plotting
word_freq = pd.DataFrame.from_records([x for x in counter.most_common(30) if len(x[0])> 2], columns = ['tokens', 'count'])
# frequency distribution of the top 19 most used words in the twitter data
word_freq.plot(kind = 'bar', x = 'tokens')
Out[18]:
<matplotlib.axes._subplots.AxesSubplot at 0x10f300f50>
In [19]:
# tokens without stop words and single and double lettered words
tokens_nsw = [x for x in remove_stopwords(tokens) if len(x) > 2]
bigrams = nltk.bigrams(tokens_nsw)
bi_count = Counter(bigrams)
#creating a data frame for the most common words without stop words in the tweets for plotting
bigram_freq = pd.DataFrame.from_records(bi_count.most_common(20), columns = ['tokens', 'count'])
bigram_freq.plot(kind = 'bar', x = 'tokens')
Out[19]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a2f8bda90>

3. Visualizaton of geo-tagged tweets

Geo-location is another important feature of social media. Location of social media can be used in understanding urban mobilities, xxx and xxx. In emergency management, social media data can be used as crowdsourcing tool to collect real-time information. In this section, we will subset tweets with geotagged location to identified the effected areas may suffer flood or storms in the early spring 2020. Because of the limted time of supervision, we will use pre-collected data (data collected in a week) to demonstrate how to process and visualize geo-location of tweets.

Data

  • wards
  • population
  • flood

Process

  • ward and population join
  • summarise
  • calculation
  • visualization
  1. It is suggested to create a folder and name it as rm03_YourCRSid_sup2, at your prefered directory on your disk. This folder will be the working directory for the assignment and supervision.
  2. Download dataset (sample data of Sejong, South Korea): Urban_2018.zip, Exclusion_2014.zip, Road_2018.zip, and Boundary.zip in the working directory (you don’t need to unzip).

QGIS Project Setup (5 mins)

  1. In the menu bar, Click Project > New to create a new QGIS project.
  2. Go to Project > Save As and save as supervision1.QGZ to the working directory.
  3. Go to Project > Properties and open the Project Properties window.
    • General tab: in the general settings, set your working directory as Project Home, change the unit for distance measurement you prefer and also display coordinates units.
    • Metadata tab: It is suggested to input title, author, creation date and a short abstract in the identification tab.
    • CRS tab: this tab provides Coordinate Reference System (CRS) setting for the project file. Here, we choose the projected coordinate system, OSGB 1936/British National Grid EPSG:27700. Be aware that CRS setting in the Project Properties is just for the project (called Data Frame setting in ArcGIS). CRS setting for layers will be introduced later.
      Note: after adding Project home, you can find Project Home directory is showing in the Browser panel. It is much easier to locate your data files through this panel.

Making heatmap based on their location (5Mins)

  • How to import data from spreadsheets and CSV with coordinates?
  • How to display coordinates from spreadsheets and CSV in QGIS?

Importing spreadsheets or CSV files

  1. Download York_floods_tweets data of Cambridgeshire from: Cambridgeshire Insight Open Data{:target="_blank"} and save into your working directory. This is a set of data to be used to geo-locate a shortlist of agencies and facilities around Cambridge.
  2. Navigate to menu bar click Layer > Add Layer > Add Delimited Text Layer. Browse the York_floods_tweets.csv just downloaded and change the layer name to York_floods_tweets. In the section of File Format, choose CSV. In the Geometry Definition section, choose Point coordinates and select Longitude and Latitude fields as X Y fields respectively. Normally the Geometry definition section will be auto-populated if it finds a suitable X and Y coordinate fields. Then choose the right CRS (EPSG:4326 - WGS84) for this file. Finally, click add and you will find a point layer.

csv

Making heatmap for geotagged tweets

  1. Find QuickMapServices function in the Web section from the menu bar. Choose OSM-OSM Standard, and you will add base map in QGIS.

  2. Double click the York_floods_tweets layer to get in properties dialog.

  3. In the Properties dialog, switch to the Style tab. Select Heatmap as the renderer. You have a lot of choice of color-ramps for the heatmap.
  4. Set 10 millermeter for radius and click OK.

heatmap

Using print layout

The Layout Manager

QGIS allows you to create multiple maps using the same map file. For this reason, it has a tool called the Layout Manager.

  1. Click on the Project -> Layout Manager menu entry to open this tool. You’ll see a blank Layout manager dialog appear.
  2. Click the Add button and give the new layout the name of York_floods_tweets_heatmap.
  3. Click OK.
  4. Click the Show button.

layout

Basic Map Composition

In this example, the composition was already the way we wanted it. Ensure that yours is as well.

  1. In the Print Layout window, check that the values under Composition ‣ Paper and Quality are set to the following:
Size: A4 (210x297mm)
Orientation: Landscape
Quality: 300dpi
  1. Now you’ve got the page layout the way you wanted it, but this page is still blank. To add the map, click on the Add New Map button: add_map. With this tool activated, you’ll be able to place a map on the page.

  2. Click and drag a box on the blank page: layout. The map will appear on the page.

  3. Move the map by clicking and dragging it around: layout

Note:

  • Zoom in and out on the page (but not the map!) by using these buttons layout_zoom.
  • Zoom and pan the map in the main QGIS window. You can also pan the map using the Move item content tool: move Item Content layout_zoom.

Because a Layout in QGIS is part of the main map file, you’ll need to save your main project. Go to the main QGIS window (the one with the Layers panel and all the other familiar elements you were working with before), and save your project from there as usual.

Adding Map Elements

Now your map is looking good on the page, but your readers/users are not being told what’s going on yet. They need some context, which is what you’ll provide for them by adding map elements. First, let’s add a title.

Add Title

  1. Click on this button: label
  2. Click on the page, above the map, and a label will appear at the top of the map. Resize it and place it in the top center of the page. It can be resized and moved in the same way that you resized and moved the map.
  3. As you move the title, you’ll notice that guidelines appear to help you position the title in the center of the page. title

Add North Arrow

  1. Click on this button: label
  2. Click on the page, above the map, and a label will appear at the top of the map. Resize it and place it in the top center of the page. It can be resized and moved in the same way that you resized and moved the map.
  3. As you move the title, you’ll notice that guidelines appear to help you position the title in the center of the page. arrow

Add Scale

  1. Click on this button: label
  2. Click on the page, above the map, and a label will appear at the top of the map. Resize it and place it in the top center of the page. It can be resized and moved in the same way that you resized and moved the map.
  3. As you move the title, you’ll notice that guidelines appear to help you position the title in the center of the page. scale

Add Legend

  1. Click on this button: addLegend
  2. Click on the page to place the legend, and move it to where you want it: legend1

Not everything on the legend is necessary, so let’s remove some unwanted items.

In the Item Properties tab, you’ll find the Legend items panel.

  1. Select the buildings entry.
  2. Delete it from the legend by clicking the minus button: signMinus

You can also rename items.Select a layer from the same list.

  1. Click the Edit button: edit
  2. Rename the layers to Places, Roads and Streets, Surafce Water, and Rivers. Set landuse to Hidden, then click the down arrow and edit each category to name them on the legend. You can also reorder the items: legend2

Adding elements is the basic step for a map. To make a good map, you need to do more! Check the experts' guidence from ESRI illustrating how to make a meaning map by answeing 10 questions. good_map.

Exporting Your Map

Finally the map is ready for export!

  1. Find export buttons near the top left corner of the Layout window:
  2. Choose Export as Image map
In [ ]: